English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the paper, we propose a robust real-time visual odometry in dynamicenvironments via rigid-motion model updated by scene flow. The proposedalgorithm consists of spatial motion segmentation and temporal motion tracking.The spatial segmentation first generates several motion hypotheses by using agrid-based scene flow and clusters the extracted motion hypotheses, separatingobjects that move independently of one another. Further, we use a dual-modemotion model to consistently distinguish between the static and dynamic partsin the temporal motion tracking stage. Finally, the proposed algorithmestimates the pose of a camera by taking advantage of the region classified asstatic parts. In order to evaluate the performance of visual odometry under theexistence of dynamic rigid objects, we use self-collected dataset containingRGB-D images and motion capture data for ground-truth. We compare our algorithmwith state-of-the-art visual odometry algorithms. The validation resultssuggest that the proposed algorithm can estimate the pose of a camera robustlyand accurately in dynamic environments.

Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model